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Tuning a Simulated Annealing metaheuristic for cross-domain search


Warren G. Jackson


Robert John


Simulated Annealing is a well known local search metaheuristic used for solving computationally hard optimization problems. Cross-domain search poses a higher level issue where a single solution method is used with minor, preferably no modification for solving characteristically different optimisation problems. The performance of a metaheuristic is often dependant on its initial parameter settings, hence detecting the best configuration, i.e. parameter tuning is crucial, which becomes a further challenge for cross-domain search. In this paper, we investigate the cross-domain search performance of Simulated Annealing via tuning for solving six problems, ranging from personnel scheduling to vehicle routing under a stochastic local search framework. The empirical results show that Simulated Annealing is extremely sensitive to the initial parameter settings leading to sub-standard performance when used as a single solution method for cross-domain search. Moreover, we demonstrate that cross-domain parameter tuning is inferior to domain-level tuning highlighting the requirements for adaptive parameter configurations when dealing with cross-domain search.


Jackson, W. G., Özcan, E., & John, R. (in press). Tuning a Simulated Annealing metaheuristic for cross-domain search.

Conference Name IEEE Congress on Evolutionary Computation 2017
End Date Jun 9, 2017
Acceptance Date Mar 15, 2017
Online Publication Date Jul 7, 2017
Deposit Date Mar 21, 2017
Publicly Available Date Jul 7, 2017
Peer Reviewed Peer Reviewed
Keywords Tuning, Cooling, Search problems, Simulated annealing, Schedules
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Additional Information Published in: 2017 IEEE Congress on Evolutionary Computation (CEC) : 5-8 June 2017 ISBN: 978-1-5090-4601-0. pp. 1055-1062, doi:10.1109/CEC.2017.7969424 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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